Effect of bloodletting therapy at local myofascial trigger points and acupuncture at Jiaji (EX-B 2) points on upper back myofascial pain syndrome: a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To observe the clinical efficacy of bloodletting therapy and acupuncture at Jiaji points for treating upper back myofascial pain syndrome (MPS), and compare this with lidocaine block therapy. METHODS: A total of 66 upper back MPS patients were randomly assigned to either the treatment group or the control group in a 1: 1 ratio. The treatment group (n = 33) were treated with bloodletting therapy at local myofascial trigger points and acupuncture at Jiaji (EX-B 2) points; one treatment course consisted of five, single 20-min-treatments with a 2-day break between each treatment. The control group (n = 33) were treated with a lidocaine block at trigger points; one treatment course consisted of five sessions of lidocaine block therapy with a 2-day break between each session. The simplified McGill Scale (SF-MPQ) and tenderness threshold determination were used to assess pain before and after a course of treatment. RESULTS: After the third and fifth treatment, the SF-MPQ values were significantly decreased (P < 0.01) and the tenderness thresholds were significantly increased (P < 0.01) in both groups compared with before treatment. There were no significant differences in pain assessments between the two groups after three and five treatments (P > 0.05). There were five cases with minor adverse reactions reported in the control patients, while no adverse reactions were reported in the treatment group. CONCLUSION: Bloodletting therapy at local myofascial trigger points and acupuncture at Jiaji points was effective in treating upper back MPS. Clinically, bloodletting and acupuncture therapy had the same efficacy as the lidocaine block therapy, with fewer adverse reactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".